Pernice Simone, Maglione Alessandro, Tortarolo Dora, Sirovich Roberta, Clerico Marinella, Rolla Simona, Beccuti Marco, Cordero Francesca
Department of Computer Science, University of Turin, Corso Svizzera 185, Turin, 10149, Italy; CINI Infolife laboratory, Turin, Italy.
Department of Clinical and Biological Sciences, Regione Gonzole 10, Orbassano, 10043, Italy.
J Biomed Inform. 2023 Dec;148:104546. doi: 10.1016/j.jbi.2023.104546. Epub 2023 Nov 19.
Computational models are at the forefront of the pursuit of personalized medicine thanks to their descriptive and predictive abilities. In the presence of complex and heterogeneous data, patient stratification is a prerequisite for effective precision medicine, since disease development is often driven by individual variability and unpredictable environmental events. Herein, we present GreatNectorworkflow as a valuable tool for (i) the analysis and clustering of patient-derived longitudinal data, and (ii) the simulation of the resulting model of patient-specific disease dynamics.
GreatNectoris designed by combining an analytic strategy composed of CONNECTOR, a data-driven framework for the inspection of longitudinal data, and an unsupervised methodology to stratify the subjects with GreatMod, a quantitative modeling framework based on the Petri Net formalism and its generalizations.
To illustrate GreatNectorcapabilities, we exploited longitudinal data of four immune cell populations collected from Multiple Sclerosis patients. Our main results report that the T-cell dynamics after alemtuzumab treatment separate non-responders versus responders patients, and the patients in the non-responders group are characterized by an increase of the Th17 concentration around 36 months.
GreatNectoranalysis was able to stratify individual patients into three model meta-patients whose dynamics suggested insight into patient-tailored interventions.
计算模型凭借其描述和预测能力处于个性化医疗追求的前沿。在存在复杂且异质性数据的情况下，患者分层是有效精准医疗的先决条件，因为疾病发展往往由个体变异性和不可预测的环境事件驱动。在此，我们展示GreatNector工作流程作为一种有价值的工具，用于（i）对患者来源的纵向数据进行分析和聚类，以及（ii）模拟所得的患者特异性疾病动态模型。
GreatNector通过结合一种分析策略来设计，该策略由CONNECTOR（一种用于检查纵向数据的数据驱动框架）和一种无监督方法组成，用于使用GreatMod对受试者进行分层，GreatMod是一个基于Petri网形式主义及其推广的定量建模框架。
为了说明GreatNector的能力，我们利用了从多发性硬化症患者收集的四个免疫细胞群体的纵向数据。我们的主要结果表明，阿仑单抗治疗后的T细胞动态将无反应者与有反应者区分开来，无反应者组的患者在约36个月时Th17浓度增加。
GreatNector分析能够将个体患者分层为三个模型元患者，其动态为患者定制干预提供了见解。
